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Post-launch diagnostic: why is it underperforming?

The launch is missing plan and the room has five theories. Decompose the miss before choosing a fix.

The prompt — copy and run it

You are a launch turnaround analyst. I will paste actuals vs plan for a struggling new item — distribution, velocity, pricing, promotion, retailer mix. Produce:

A) A MISS DECOMPOSITION from my numbers: how much of the gap to plan is distribution (doors never materialized), velocity (doors exist, turns do not), or price/promo execution — shown as a simple bridge.
B) A ROOT-CAUSE TREE for the dominant branch: awareness → findability (shelf placement, search) → conversion (price, pack, claims) → repeat (product experience) — with the specific, checkable evidence for each node.
C) A 30-DAY FIX PLAN ordered cheapest-first, plus honest KILL CRITERIA: the reading at which continued support is throwing good money after bad.

My data: [PASTE: plan vs actual by measure, retailer-level detail if available, promo/pricing history since launch]

Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in the data I give you, write "not provided" and flag it. Mark every claim I should verify against my syndicated data or internal reporting before using it externally. Never include retailer-confidential terms or personally identifiable shopper data.

How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Add your context. This prompt runs as-is — paste it, then add the specific details, data, or files it should reason over.
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Underperforming launches attract fixes before diagnoses — media gets blamed when the item never hit the shelf, the shelf gets blamed when repeat is broken. The bridge-first structure forces the room to agree on WHERE the miss lives before debating what to do about it, and kill criteria make the hardest call in advance.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

The launch is missing plan and the room has five theories. Decompose the miss before choosing a fix.

Why does this prompt work?

Underperforming launches attract fixes before diagnoses — media gets blamed when the item never hit the shelf, the shelf gets blamed when repeat is broken. The bridge-first structure forces the room to agree on WHERE the miss lives before debating what to do about it, and kill criteria make the hardest call in advance.

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PromptSharp prompts are drafted with AI assistance and human-reviewed. They structure how a model reasons over data you provide — they do not source or verify facts for you, and you own every output. Nothing here is financial, legal, tax, or investment advice. Never paste confidential, client, or material non-public information into consumer AI tools; follow your employer's AI-use policy. © 2026 PromptSharp.